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Titlebook: Data Science for Nano Image Analysis; Chiwoo Park,Yu Ding Book 2021 Springer Nature Switzerland AG 2021 Nano Image Analysis.Data Science.M

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書目名稱Data Science for Nano Image Analysis
編輯Chiwoo Park,Yu Ding
視頻videohttp://file.papertrans.cn/264/263120/263120.mp4
概述Applies Data Science and OR techniques to Nano Image Analysis.Showcases specific nano material problems with the appropriate set of data science methods.Includes real-time dynamic modeling and object
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Data Science for Nano Image Analysis;  Chiwoo Park,Yu Ding Book 2021 Springer Nature Switzerland AG 2021 Nano Image Analysis.Data Science.M
描述.This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization..This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, gr
出版日期Book 2021
關(guān)鍵詞Nano Image Analysis; Data Science; Material Science; Image Pattern Analysis; Shape Analysis; Object Track
版次1
doihttps://doi.org/10.1007/978-3-030-72822-9
isbn_softcover978-3-030-72824-3
isbn_ebook978-3-030-72822-9Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Chain Maps and Reduction Algorithms Material structures are often imaged and characterized using microscopy techniques and described by the outlines of material interior and exterior structures, as well as the geometric features of these outlines, such as sizes, shapes and topology. To obtain the outlines, one segments a microscope i
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Prospects in Digital Image Processing objects are initiating, crystalizing, and morphing into different sizes and shapes. To enable in-process control of nanocrystal production, this technology innovation needs a data science solution addressing the capability of online tracking of the underlying stochastic process of nanocrystal growt
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J. M. Cadenas,M. C. Garrido,R. Martínez purpose of studying and designing novel materials. Studies of the dynamic evolutions are enabled through analyzing a sequence of microscopic images taken at different times or different stages of the evolutions. The time-resolved imaging can be acquired using in situ material imaging techniques, re
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Garrett Nicolai,Robert Hildermancontain the structures of material objects that change over the course of a material process. If one is interested in knowing how a population of material objects is collectively evolved in their sizes and shapes, a probability distribution function of the shapes and sizes ought to be tracked. Towar
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